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# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for resnet."""
# Import libraries
from absl.testing import parameterized
import numpy as np
import tensorflow as tf, tf_keras
from tensorflow.python.distribute import combinations
from tensorflow.python.distribute import strategy_combinations
from official.vision.modeling.backbones import resnet
class ResNetTest(parameterized.TestCase, tf.test.TestCase):
@parameterized.parameters(
(128, 10, 1),
(128, 18, 1),
(128, 26, 1),
(128, 34, 1),
(128, 50, 4),
(128, 101, 4),
(128, 152, 4),
)
def test_network_creation(self, input_size, model_id,
endpoint_filter_scale):
"""Test creation of ResNet family models."""
resnet_params = {
10: 4915904,
18: 11190464,
26: 17465024,
34: 21306048,
50: 23561152,
101: 42605504,
152: 58295232,
}
tf_keras.backend.set_image_data_format('channels_last')
network = resnet.ResNet(model_id=model_id)
self.assertEqual(network.count_params(), resnet_params[model_id])
inputs = tf_keras.Input(shape=(input_size, input_size, 3), batch_size=1)
endpoints = network(inputs)
self.assertAllEqual(
[1, input_size / 2**2, input_size / 2**2, 64 * endpoint_filter_scale],
endpoints['2'].shape.as_list())
self.assertAllEqual(
[1, input_size / 2**3, input_size / 2**3, 128 * endpoint_filter_scale],
endpoints['3'].shape.as_list())
self.assertAllEqual(
[1, input_size / 2**4, input_size / 2**4, 256 * endpoint_filter_scale],
endpoints['4'].shape.as_list())
self.assertAllEqual(
[1, input_size / 2**5, input_size / 2**5, 512 * endpoint_filter_scale],
endpoints['5'].shape.as_list())
@combinations.generate(
combinations.combine(
strategy=[
strategy_combinations.cloud_tpu_strategy,
strategy_combinations.one_device_strategy_gpu,
],
use_sync_bn=[False, True],
))
def test_sync_bn_multiple_devices(self, strategy, use_sync_bn):
"""Test for sync bn on TPU and GPU devices."""
inputs = np.random.rand(64, 128, 128, 3)
tf_keras.backend.set_image_data_format('channels_last')
with strategy.scope():
network = resnet.ResNet(model_id=50, use_sync_bn=use_sync_bn)
_ = network(inputs)
@parameterized.parameters(
(128, 34, 1, 'v0', None, 0.0, 1.0, False, False),
(128, 34, 1, 'v1', 0.25, 0.2, 1.25, True, True),
(128, 50, 4, 'v0', None, 0.0, 1.5, False, False),
(128, 50, 4, 'v1', 0.25, 0.2, 2.0, True, True),
)
def test_resnet_rs(self, input_size, model_id, endpoint_filter_scale,
stem_type, se_ratio, init_stochastic_depth_rate,
depth_multiplier, resnetd_shortcut, replace_stem_max_pool):
"""Test creation of ResNet family models."""
tf_keras.backend.set_image_data_format('channels_last')
network = resnet.ResNet(
model_id=model_id,
depth_multiplier=depth_multiplier,
stem_type=stem_type,
resnetd_shortcut=resnetd_shortcut,
replace_stem_max_pool=replace_stem_max_pool,
se_ratio=se_ratio,
init_stochastic_depth_rate=init_stochastic_depth_rate)
inputs = tf_keras.Input(shape=(input_size, input_size, 3), batch_size=1)
_ = network(inputs)
@parameterized.parameters(1, 3, 4)
def test_input_specs(self, input_dim):
"""Test different input feature dimensions."""
tf_keras.backend.set_image_data_format('channels_last')
input_specs = tf_keras.layers.InputSpec(shape=[None, None, None, input_dim])
network = resnet.ResNet(model_id=50, input_specs=input_specs)
inputs = tf_keras.Input(shape=(128, 128, input_dim), batch_size=1)
_ = network(inputs)
def test_serialize_deserialize(self):
# Create a network object that sets all of its config options.
kwargs = dict(
model_id=50,
depth_multiplier=1.0,
stem_type='v0',
se_ratio=None,
resnetd_shortcut=False,
replace_stem_max_pool=False,
init_stochastic_depth_rate=0.0,
scale_stem=True,
use_sync_bn=False,
activation='relu',
norm_momentum=0.99,
norm_epsilon=0.001,
kernel_initializer='VarianceScaling',
kernel_regularizer=None,
bias_regularizer=None,
bn_trainable=True)
network = resnet.ResNet(**kwargs)
expected_config = dict(kwargs)
self.assertEqual(network.get_config(), expected_config)
# Create another network object from the first object's config.
new_network = resnet.ResNet.from_config(network.get_config())
# Validate that the config can be forced to JSON.
_ = new_network.to_json()
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(network.get_config(), new_network.get_config())
if __name__ == '__main__':
tf.test.main()
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